Agradecimentos: This work was carried out in association with the project registered as ‘BG‐07: Reduction of uncertainties through the incorporation of 4D seismic data in the modeling of the reservoir’ (University of Campinas [UNICAMP]/BG Brazil/ANP) funded by BG E&P Brasil Ltda. (a subsidiary of...

Agradecimentos: This work was carried out in association with the project registered as ‘BG‐07: Reduction of uncertainties through the incorporation of 4D seismic data in the modeling of the reservoir’ (University of Campinas [UNICAMP]/BG Brazil/ANP) funded by BG E&P Brasil Ltda. (a subsidiary of Shell) under the ANP research and development levy as ‘Investment Commitment to Research and Development’. The authors thank UNISIM, DEP‐FEM‐UNICAMP, Center for Petroleum Studies (CEPETRO) and Energi Simulation for supporting this work and CGG, Schlumberger and CMG for software licenses. We also would like to thank Statoil (operator of Norne Field) and its licence partners ENI and Petoro for the release of the Norne Field data. The authors acknowledge the Center for Integrated Operations at NTNU for cooperation and coordination of the Norne cases. The view expressed in this paper are the views of the authors and do not necessarily reflect the views of Statoil and the Norne licence partners

Abstract: The ultimate goal of reservoir simulation in reservoir surveillance technology is to estimate long-term production forecasting and to plan development and management of petroleum fields. However, maintaining reliable reservoir models which honour available static and dynamic data, involve...

Abstract: The ultimate goal of reservoir simulation in reservoir surveillance technology is to estimate long-term production forecasting and to plan development and management of petroleum fields. However, maintaining reliable reservoir models which honour available static and dynamic data, involve inherent risks due to the uncertainties in space and time of the distribution of hydrocarbons inside reservoirs. Recent applications have shown that these uncertainties can be reduced by quantitative integration of seismic data into the reservoir modelling workflows to identify which areas and reservoir attributes of the model should be updated. This work aims using seismic data to reduce ambiguity in calibrating reservoir flow simulation model with an uncertain petro-elastic model, proposing a circular workflow of inverted seismic impedance (3D and 4D) and engineering studies, with emphasis on the interface between static and dynamic models. The main contribution is to develop an updating procedure for adjusting reservoir simulation response before using it in the production forecasting and enhance the interpretive capability of reservoir properties. Accordingly, the workflow evaluates consistency of reservoir simulation model and inverted seismic impedance, assisted by production history data, to close the loop between reservoir engineering and seismic domains. The methodology is evaluated in a complex, faulted, sandstone reservoir, the Norne benchmark field, where a significant reservoir behaviour understanding (about the static and dynamic reservoir properties) is obtained towards the quantitative integration of seismic impedance data. This leads to diagnosis of the reservoir flow simulation reliability and generation of an updated simulation model consistent with observed seismic and well production history data, as well as a calibrated petro-elastic model. Furthermore, as Norne Field is a benchmark case, this study can be considered to enrich the discussions over deterministic or probabilistic history matching studies

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